Preventing antisocial robots: A pathway to artificial empathy.

Given the accelerating powers of artificial intelligence (AI), we must equip artificial agents and robots with empathy to prevent harmful and irreversible decisions. Current approaches to artificial empathy focus on its cognitive or performative processes, overlooking affect, and thus promote sociopathic behaviors. Artificially vulnerable, fully empathic AI is necessary to prevent sociopathic robots and protect human welfare.

Seeing objects improves our hearing of the sounds they make.

It has been established that lip reading improves the perception of auditory speech. But does seeing objects themselves help us hear better the sounds they make? Here we report a series of psychophysical experiments in humans showing that the visual enhancement of auditory sensitivity is not confined to speech. We further show that the crossmodal enhancement was associated with the conscious visualization of the stimulus: we can better hear the sounds an object makes when we are conscious of seeing that object.

Decoding the neural representation of story meanings across languages.

Drawing from a common lexicon of semantic units, humans fashion narratives whose meaning transcends that of their individual utterances. However, while brain regions that represent lower-level semantic units, such as words and sentences, have been identified, questions remain about the neural representation of narrative comprehension, which involves inferring cumulative meaning. To address these questions, we exposed English, Mandarin, and Farsi native speakers to native language translations of the same stories during fMRI scanning.

Convergent and invariant object representations for sight, sound, and touch.

We continuously perceive objects in the world through multiple sensory channels. In this study, we investigated the convergence of information from different sensory streams within the cerebral cortex. We presented volunteers with three common objects via three different modalities-sight, sound, and touch-and used multivariate pattern analysis of functional magnetic resonance imaging data to map the cortical regions containing information about the identity of the objects.

Multivariate cross-classification: applying machine learning techniques to characterize abstraction in neural representations.

Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC), and review several domains where it has recently made an impact.

Neural convergence and divergence in the mammalian cerebral cortex: from experimental neuroanatomy to functional neuroimaging.

A development essential for understanding the neural basis of complex behavior and cognition is the description, during the last quarter of the twentieth century, of detailed patterns of neuronal circuitry in the mammalian cerebral cortex. This effort established that sensory pathways exhibit successive levels of convergence, from the early sensory cortices to sensory-specific and multisensory association cortices, culminating in maximally integrative regions. It was also established that this convergence is reciprocated by successive levels of divergence, from the maximally integrative areas all the way back to the early sensory cortices.

Sight and sound converge to form modality-invariant representations in temporoparietal cortex.

People can identify objects in the environment with remarkable accuracy, regardless of the sensory modality they use to perceive them. This suggests that information from different sensory channels converges somewhere in the brain to form modality-invariant representations, i.e.

Glutamatergic contributions to nicotinic acetylcholine receptor agonist-evoked cholinergic transients in the prefrontal cortex.

Because modulation of cortical cholinergic neurotransmission has been hypothesized to represent a necessary mechanism mediating the beneficial cognitive effects of nicotine and nicotinic acetylcholine receptor (nAChR) subtype-selective agonists, we used choline-sensitive microelectrodes for the real-time measurement of ACh release in vivo, to characterize cholinergic transients evoked by nicotine and the alpha4beta2*-selective nAChR partial agonist 2-methyl-3-(2-(S)-pyrrolindinylmethoxy)pyridine dihydrochloride (ABT-089), a clinically effective cognition enhancer. In terms of cholinergic signal amplitudes, ABT-089 was significantly more potent than nicotine in evoking ACh cholinergic transients. Moreover, cholinergic signals evoked by ABT-089 were characterized by faster signal rise time and decay rate.